In [1]:
!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install --upgrade plotly
Requirement already satisfied: yfinance in /usr/local/lib/python3.12/dist-packages (0.2.66) Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.2.2) Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.0.2) Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.32.4) Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.12/dist-packages (from yfinance) (0.0.12) Requirement already satisfied: platformdirs>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (4.4.0) Requirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2025.2) Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.4.6) Requirement already satisfied: peewee>=3.16.2 in /usr/local/lib/python3.12/dist-packages (from yfinance) (3.18.2) Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.12/dist-packages (from yfinance) (4.13.5) Requirement already satisfied: curl_cffi>=0.7 in /usr/local/lib/python3.12/dist-packages (from yfinance) (0.13.0) Requirement already satisfied: protobuf>=3.19.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (5.29.5) Requirement already satisfied: websockets>=13.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (15.0.1) Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (2.8) Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (4.15.0) Requirement already satisfied: cffi>=1.12.0 in /usr/local/lib/python3.12/dist-packages (from curl_cffi>=0.7->yfinance) (2.0.0) Requirement already satisfied: certifi>=2024.2.2 in /usr/local/lib/python3.12/dist-packages (from curl_cffi>=0.7->yfinance) (2025.8.3) Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0) Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.3.0->yfinance) (2025.2) Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.31->yfinance) (3.4.3) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.31->yfinance) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.31->yfinance) (2.5.0) Requirement already satisfied: pycparser in /usr/local/lib/python3.12/dist-packages (from cffi>=1.12.0->curl_cffi>=0.7->yfinance) (2.23) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->yfinance) (1.17.0)
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In [2]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [3]:
import plotly.io as pio
pio.renderers.default = "iframe"
In [4]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)
In [5]:
def make_graph(stock_data, revenue_data, stock):
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
fig.update_layout(showlegend=False,
height=900,
title=stock,
xaxis_rangeslider_visible=True)
fig.show()
from IPython.display import display, HTML
fig_html = fig.to_html()
display(HTML(fig_html))
In [6]:
tesla = yf.Ticker("TSLA")
tesla
Out[6]:
yfinance.Ticker object <TSLA>
In [7]:
tesla_data = tesla.history(period="max")
In [8]:
tesla_data.reset_index(inplace=True)
In [9]:
tesla_data.head()
Out[9]:
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2010-06-29 00:00:00-04:00 | 1.266667 | 1.666667 | 1.169333 | 1.592667 | 281494500 | 0.0 | 0.0 |
| 1 | 2010-06-30 00:00:00-04:00 | 1.719333 | 2.028000 | 1.553333 | 1.588667 | 257806500 | 0.0 | 0.0 |
| 2 | 2010-07-01 00:00:00-04:00 | 1.666667 | 1.728000 | 1.351333 | 1.464000 | 123282000 | 0.0 | 0.0 |
| 3 | 2010-07-02 00:00:00-04:00 | 1.533333 | 1.540000 | 1.247333 | 1.280000 | 77097000 | 0.0 | 0.0 |
| 4 | 2010-07-06 00:00:00-04:00 | 1.333333 | 1.333333 | 1.055333 | 1.074000 | 103003500 | 0.0 | 0.0 |
In [10]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
In [11]:
html_data = requests.get(url).text
In [12]:
soup = BeautifulSoup(html_data, 'html.parser')
soup
Out[12]:
<!DOCTYPE html>
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<ul style='margin-top:10px;'>
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<meta content="Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services. Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.
<ul style='margin-top:10px;'>
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<ul style='margin-top:10px;'>
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<h2 style="margin-left:0px; font-weight:600; color:#444;">Tesla Revenue 2010-2022 | TSLA</h2>
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<span style="color:#444; line-height: 1.8;">Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services. Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.
<ul style="margin-top:10px;">
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<th colspan="2" style="text-align:center">Tesla Annual Revenue<br/><span style="font-size:14px;">(Millions of US $)</span></th>
</tr>
</thead>
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<tr>
<td style="text-align:center">2021</td>
<td style="text-align:center">$53,823</td>
</tr>
<tr>
<td style="text-align:center">2020</td>
<td style="text-align:center">$31,536</td>
</tr>
<tr>
<td style="text-align:center">2019</td>
<td style="text-align:center">$24,578</td>
</tr>
<tr>
<td style="text-align:center">2018</td>
<td style="text-align:center">$21,461</td>
</tr>
<tr>
<td style="text-align:center">2017</td>
<td style="text-align:center">$11,759</td>
</tr>
<tr>
<td style="text-align:center">2016</td>
<td style="text-align:center">$7,000</td>
</tr>
<tr>
<td style="text-align:center">2015</td>
<td style="text-align:center">$4,046</td>
</tr>
<tr>
<td style="text-align:center">2014</td>
<td style="text-align:center">$3,198</td>
</tr>
<tr>
<td style="text-align:center">2013</td>
<td style="text-align:center">$2,013</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">$413</td>
</tr>
<tr>
<td style="text-align:center">2011</td>
<td style="text-align:center">$204</td>
</tr>
<tr>
<td style="text-align:center">2010</td>
<td style="text-align:center">$117</td>
</tr>
<tr>
<td style="text-align:center">2009</td>
<td style="text-align:center">$112</td>
</tr>
</tbody>
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<th colspan="2" style="text-align:center">Tesla Quarterly Revenue<br/><span style="font-size:14px;">(Millions of US $)</span></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">2022-09-30</td>
<td style="text-align:center">$21,454</td>
</tr>
<tr>
<td style="text-align:center">2022-06-30</td>
<td style="text-align:center">$16,934</td>
</tr>
<tr>
<td style="text-align:center">2022-03-31</td>
<td style="text-align:center">$18,756</td>
</tr>
<tr>
<td style="text-align:center">2021-12-31</td>
<td style="text-align:center">$17,719</td>
</tr>
<tr>
<td style="text-align:center">2021-09-30</td>
<td style="text-align:center">$13,757</td>
</tr>
<tr>
<td style="text-align:center">2021-06-30</td>
<td style="text-align:center">$11,958</td>
</tr>
<tr>
<td style="text-align:center">2021-03-31</td>
<td style="text-align:center">$10,389</td>
</tr>
<tr>
<td style="text-align:center">2020-12-31</td>
<td style="text-align:center">$10,744</td>
</tr>
<tr>
<td style="text-align:center">2020-09-30</td>
<td style="text-align:center">$8,771</td>
</tr>
<tr>
<td style="text-align:center">2020-06-30</td>
<td style="text-align:center">$6,036</td>
</tr>
<tr>
<td style="text-align:center">2020-03-31</td>
<td style="text-align:center">$5,985</td>
</tr>
<tr>
<td style="text-align:center">2019-12-31</td>
<td style="text-align:center">$7,384</td>
</tr>
<tr>
<td style="text-align:center">2019-09-30</td>
<td style="text-align:center">$6,303</td>
</tr>
<tr>
<td style="text-align:center">2019-06-30</td>
<td style="text-align:center">$6,350</td>
</tr>
<tr>
<td style="text-align:center">2019-03-31</td>
<td style="text-align:center">$4,541</td>
</tr>
<tr>
<td style="text-align:center">2018-12-31</td>
<td style="text-align:center">$7,226</td>
</tr>
<tr>
<td style="text-align:center">2018-09-30</td>
<td style="text-align:center">$6,824</td>
</tr>
<tr>
<td style="text-align:center">2018-06-30</td>
<td style="text-align:center">$4,002</td>
</tr>
<tr>
<td style="text-align:center">2018-03-31</td>
<td style="text-align:center">$3,409</td>
</tr>
<tr>
<td style="text-align:center">2017-12-31</td>
<td style="text-align:center">$3,288</td>
</tr>
<tr>
<td style="text-align:center">2017-09-30</td>
<td style="text-align:center">$2,985</td>
</tr>
<tr>
<td style="text-align:center">2017-06-30</td>
<td style="text-align:center">$2,790</td>
</tr>
<tr>
<td style="text-align:center">2017-03-31</td>
<td style="text-align:center">$2,696</td>
</tr>
<tr>
<td style="text-align:center">2016-12-31</td>
<td style="text-align:center">$2,285</td>
</tr>
<tr>
<td style="text-align:center">2016-09-30</td>
<td style="text-align:center">$2,298</td>
</tr>
<tr>
<td style="text-align:center">2016-06-30</td>
<td style="text-align:center">$1,270</td>
</tr>
<tr>
<td style="text-align:center">2016-03-31</td>
<td style="text-align:center">$1,147</td>
</tr>
<tr>
<td style="text-align:center">2015-12-31</td>
<td style="text-align:center">$1,214</td>
</tr>
<tr>
<td style="text-align:center">2015-09-30</td>
<td style="text-align:center">$937</td>
</tr>
<tr>
<td style="text-align:center">2015-06-30</td>
<td style="text-align:center">$955</td>
</tr>
<tr>
<td style="text-align:center">2015-03-31</td>
<td style="text-align:center">$940</td>
</tr>
<tr>
<td style="text-align:center">2014-12-31</td>
<td style="text-align:center">$957</td>
</tr>
<tr>
<td style="text-align:center">2014-09-30</td>
<td style="text-align:center">$852</td>
</tr>
<tr>
<td style="text-align:center">2014-06-30</td>
<td style="text-align:center">$769</td>
</tr>
<tr>
<td style="text-align:center">2014-03-31</td>
<td style="text-align:center">$621</td>
</tr>
<tr>
<td style="text-align:center">2013-12-31</td>
<td style="text-align:center">$615</td>
</tr>
<tr>
<td style="text-align:center">2013-09-30</td>
<td style="text-align:center">$431</td>
</tr>
<tr>
<td style="text-align:center">2013-06-30</td>
<td style="text-align:center">$405</td>
</tr>
<tr>
<td style="text-align:center">2013-03-31</td>
<td style="text-align:center">$562</td>
</tr>
<tr>
<td style="text-align:center">2012-12-31</td>
<td style="text-align:center">$306</td>
</tr>
<tr>
<td style="text-align:center">2012-09-30</td>
<td style="text-align:center">$50</td>
</tr>
<tr>
<td style="text-align:center">2012-06-30</td>
<td style="text-align:center">$27</td>
</tr>
<tr>
<td style="text-align:center">2012-03-31</td>
<td style="text-align:center">$30</td>
</tr>
<tr>
<td style="text-align:center">2011-12-31</td>
<td style="text-align:center">$39</td>
</tr>
<tr>
<td style="text-align:center">2011-09-30</td>
<td style="text-align:center">$58</td>
</tr>
<tr>
<td style="text-align:center">2011-06-30</td>
<td style="text-align:center">$58</td>
</tr>
<tr>
<td style="text-align:center">2011-03-31</td>
<td style="text-align:center">$49</td>
</tr>
<tr>
<td style="text-align:center">2010-12-31</td>
<td style="text-align:center">$36</td>
</tr>
<tr>
<td style="text-align:center">2010-09-30</td>
<td style="text-align:center">$31</td>
</tr>
<tr>
<td style="text-align:center">2010-06-30</td>
<td style="text-align:center">$28</td>
</tr>
<tr>
<td style="text-align:center">2010-03-31</td>
<td style="text-align:center">$21</td>
</tr>
<tr>
<td style="text-align:center">2009-12-31</td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">2009-09-30</td>
<td style="text-align:center">$46</td>
</tr>
<tr>
<td style="text-align:center">2009-06-30</td>
<td style="text-align:center">$27</td>
</tr>
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</table>
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<th style="text-align:center">Sector</th>
<th style="text-align:center">Industry</th>
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<th style="text-align:center">Revenue</th>
</tr>
</thead>
<tbody>
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<td style="text-align:center"><a href="https://www.macrotrends.net/stocks/sector/5/auto-tires-trucks">Auto/Tires/Trucks</a></td>
<td style="text-align:center"><a href="https://www.macrotrends.net/stocks/industry/7/">Auto Manufacturers - Domestic</a></td>
<td style="text-align:center">$549.575B</td>
<td style="text-align:center">$53.823B</td>
</tr>
<tr>
<td colspan="4" style="padding:15px;">
<span>Tesla is the market leader in battery-powered electric car sales in the United States, with roughly 70% market share. The company's flagship Model 3 is the best-selling EV model in the United States. Tesla, which has managed to garner the reputation of a gold standard over the years, is now a far bigger entity that what it started off since its IPO in 2010, with its market cap crossing $1 trillion for the first time in October 2021.? The EV king's market capitalization is more than the combined value of legacy automakers including Toyota, Volkswagen, Daimler, General Motors and Ford.Over the years, Tesla has shifted from developing niche products for affluent buyers to making more affordable EVs for the masses. The firm's three-pronged business model approach of direct sales, servicing, and charging its EVs sets it apart from other carmakers. Tesla, which is touted as the clean energy revolutionary automaker, is much more than just a car manufacturer.</span>
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<td style="text-align:center">United States</td>
<td style="text-align:center">$53.930B</td>
<td style="text-align:center">5.56</td>
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<td style="text-align:left"><a href="/stocks/charts/F/ford-motor/revenue">Ford Motor (F)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$52.668B</td>
<td style="text-align:center">8.09</td>
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<td style="text-align:left"><a href="/stocks/charts/HOG/harley-davidson/revenue">Harley-Davidson (HOG)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$6.762B</td>
<td style="text-align:center">9.56</td>
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<td style="text-align:left"><a href="/stocks/charts/PII/polaris/revenue">Polaris (PII)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$6.267B</td>
<td style="text-align:center">11.86</td>
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<td style="text-align:left"><a href="/stocks/charts/IAA/iaa/revenue">IAA (IAA)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$5.134B</td>
<td style="text-align:center">16.40</td>
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<td style="text-align:left"><a href="/stocks/charts/FSR/fisker/revenue">Fisker (FSR)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$2.261B</td>
<td style="text-align:center">0.00</td>
</tr>
<tr>
<td style="text-align:left"><a href="/stocks/charts/LEV/lion-electric/revenue">Lion Electric (LEV)</a></td>
<td style="text-align:center">Canada</td>
<td style="text-align:center">$0.551B</td>
<td style="text-align:center">0.00</td>
</tr>
<tr>
<td style="text-align:left"><a href="/stocks/charts/VLTA/volta/revenue">Volta (VLTA)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.071B</td>
<td style="text-align:center">0.00</td>
</tr>
<tr>
<td style="text-align:left"><a href="/stocks/charts/BRDS/bird-global/revenue">Bird Global (BRDS)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.054B</td>
<td style="text-align:center">0.00</td>
</tr>
<tr>
<td style="text-align:left"><a href="/stocks/charts/ZEV/lightning-emotors/revenue">Lightning EMotors (ZEV)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.043B</td>
<td style="text-align:center">0.00</td>
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In [13]:
table = soup.find_all('tbody')[1]
In [14]:
tesla_list = []
for variable in table.find_all("tr"):
columns = variable.find_all("td")
date = columns[0].text
revenue = columns[1].text
tesla_list.append({"Date": date, "Revenue": revenue})
tesla_revenue = pd.DataFrame(tesla_list)
tesla_revenue
Out[14]:
| Date | Revenue | |
|---|---|---|
| 0 | 2022-09-30 | $21,454 |
| 1 | 2022-06-30 | $16,934 |
| 2 | 2022-03-31 | $18,756 |
| 3 | 2021-12-31 | $17,719 |
| 4 | 2021-09-30 | $13,757 |
| 5 | 2021-06-30 | $11,958 |
| 6 | 2021-03-31 | $10,389 |
| 7 | 2020-12-31 | $10,744 |
| 8 | 2020-09-30 | $8,771 |
| 9 | 2020-06-30 | $6,036 |
| 10 | 2020-03-31 | $5,985 |
| 11 | 2019-12-31 | $7,384 |
| 12 | 2019-09-30 | $6,303 |
| 13 | 2019-06-30 | $6,350 |
| 14 | 2019-03-31 | $4,541 |
| 15 | 2018-12-31 | $7,226 |
| 16 | 2018-09-30 | $6,824 |
| 17 | 2018-06-30 | $4,002 |
| 18 | 2018-03-31 | $3,409 |
| 19 | 2017-12-31 | $3,288 |
| 20 | 2017-09-30 | $2,985 |
| 21 | 2017-06-30 | $2,790 |
| 22 | 2017-03-31 | $2,696 |
| 23 | 2016-12-31 | $2,285 |
| 24 | 2016-09-30 | $2,298 |
| 25 | 2016-06-30 | $1,270 |
| 26 | 2016-03-31 | $1,147 |
| 27 | 2015-12-31 | $1,214 |
| 28 | 2015-09-30 | $937 |
| 29 | 2015-06-30 | $955 |
| 30 | 2015-03-31 | $940 |
| 31 | 2014-12-31 | $957 |
| 32 | 2014-09-30 | $852 |
| 33 | 2014-06-30 | $769 |
| 34 | 2014-03-31 | $621 |
| 35 | 2013-12-31 | $615 |
| 36 | 2013-09-30 | $431 |
| 37 | 2013-06-30 | $405 |
| 38 | 2013-03-31 | $562 |
| 39 | 2012-12-31 | $306 |
| 40 | 2012-09-30 | $50 |
| 41 | 2012-06-30 | $27 |
| 42 | 2012-03-31 | $30 |
| 43 | 2011-12-31 | $39 |
| 44 | 2011-09-30 | $58 |
| 45 | 2011-06-30 | $58 |
| 46 | 2011-03-31 | $49 |
| 47 | 2010-12-31 | $36 |
| 48 | 2010-09-30 | $31 |
| 49 | 2010-06-30 | $28 |
| 50 | 2010-03-31 | $21 |
| 51 | 2009-12-31 | |
| 52 | 2009-09-30 | $46 |
| 53 | 2009-06-30 | $27 |
In [15]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"",regex=True)
<>:1: SyntaxWarning: invalid escape sequence '\$' <>:1: SyntaxWarning: invalid escape sequence '\$' /tmp/ipython-input-3152827612.py:1: SyntaxWarning: invalid escape sequence '\$'
In [16]:
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
tesla_revenue
Out[16]:
| Date | Revenue | |
|---|---|---|
| 0 | 2022-09-30 | 21454 |
| 1 | 2022-06-30 | 16934 |
| 2 | 2022-03-31 | 18756 |
| 3 | 2021-12-31 | 17719 |
| 4 | 2021-09-30 | 13757 |
| 5 | 2021-06-30 | 11958 |
| 6 | 2021-03-31 | 10389 |
| 7 | 2020-12-31 | 10744 |
| 8 | 2020-09-30 | 8771 |
| 9 | 2020-06-30 | 6036 |
| 10 | 2020-03-31 | 5985 |
| 11 | 2019-12-31 | 7384 |
| 12 | 2019-09-30 | 6303 |
| 13 | 2019-06-30 | 6350 |
| 14 | 2019-03-31 | 4541 |
| 15 | 2018-12-31 | 7226 |
| 16 | 2018-09-30 | 6824 |
| 17 | 2018-06-30 | 4002 |
| 18 | 2018-03-31 | 3409 |
| 19 | 2017-12-31 | 3288 |
| 20 | 2017-09-30 | 2985 |
| 21 | 2017-06-30 | 2790 |
| 22 | 2017-03-31 | 2696 |
| 23 | 2016-12-31 | 2285 |
| 24 | 2016-09-30 | 2298 |
| 25 | 2016-06-30 | 1270 |
| 26 | 2016-03-31 | 1147 |
| 27 | 2015-12-31 | 1214 |
| 28 | 2015-09-30 | 937 |
| 29 | 2015-06-30 | 955 |
| 30 | 2015-03-31 | 940 |
| 31 | 2014-12-31 | 957 |
| 32 | 2014-09-30 | 852 |
| 33 | 2014-06-30 | 769 |
| 34 | 2014-03-31 | 621 |
| 35 | 2013-12-31 | 615 |
| 36 | 2013-09-30 | 431 |
| 37 | 2013-06-30 | 405 |
| 38 | 2013-03-31 | 562 |
| 39 | 2012-12-31 | 306 |
| 40 | 2012-09-30 | 50 |
| 41 | 2012-06-30 | 27 |
| 42 | 2012-03-31 | 30 |
| 43 | 2011-12-31 | 39 |
| 44 | 2011-09-30 | 58 |
| 45 | 2011-06-30 | 58 |
| 46 | 2011-03-31 | 49 |
| 47 | 2010-12-31 | 36 |
| 48 | 2010-09-30 | 31 |
| 49 | 2010-06-30 | 28 |
| 50 | 2010-03-31 | 21 |
| 52 | 2009-09-30 | 46 |
| 53 | 2009-06-30 | 27 |
In [17]:
tesla_revenue.tail()
Out[17]:
| Date | Revenue | |
|---|---|---|
| 48 | 2010-09-30 | 31 |
| 49 | 2010-06-30 | 28 |
| 50 | 2010-03-31 | 21 |
| 52 | 2009-09-30 | 46 |
| 53 | 2009-06-30 | 27 |
In [18]:
gamestop = yf.Ticker("GME")
gamestop
Out[18]:
yfinance.Ticker object <GME>
In [19]:
gme_data = gamestop.history(period="max")
gme_data
Out[19]:
| Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 2002-02-13 00:00:00-05:00 | 1.620128 | 1.693350 | 1.603296 | 1.691667 | 76216000 | 0.0 | 0.0 |
| 2002-02-14 00:00:00-05:00 | 1.712707 | 1.716074 | 1.670626 | 1.683251 | 11021600 | 0.0 | 0.0 |
| 2002-02-15 00:00:00-05:00 | 1.683250 | 1.687458 | 1.658002 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 2002-02-19 00:00:00-05:00 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 2002-02-20 00:00:00-05:00 | 1.615920 | 1.662210 | 1.603296 | 1.662210 | 6892800 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 2025-09-17 00:00:00-04:00 | 26.350000 | 26.469999 | 25.580000 | 26.100000 | 7897100 | 0.0 | 0.0 |
| 2025-09-18 00:00:00-04:00 | 26.209999 | 26.420000 | 25.840000 | 25.879999 | 7806200 | 0.0 | 0.0 |
| 2025-09-19 00:00:00-04:00 | 25.830000 | 26.389999 | 25.790001 | 26.080000 | 9567600 | 0.0 | 0.0 |
| 2025-09-22 00:00:00-04:00 | 26.000000 | 27.340000 | 25.969999 | 26.780001 | 11263600 | 0.0 | 0.0 |
| 2025-09-23 00:00:00-04:00 | 26.905001 | 27.240000 | 26.590000 | 26.799999 | 6638965 | 0.0 | 0.0 |
5941 rows × 7 columns
In [20]:
gme_data.reset_index(inplace=True)
gme_data.head()
Out[20]:
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2002-02-13 00:00:00-05:00 | 1.620128 | 1.693350 | 1.603296 | 1.691667 | 76216000 | 0.0 | 0.0 |
| 1 | 2002-02-14 00:00:00-05:00 | 1.712707 | 1.716074 | 1.670626 | 1.683251 | 11021600 | 0.0 | 0.0 |
| 2 | 2002-02-15 00:00:00-05:00 | 1.683250 | 1.687458 | 1.658002 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 3 | 2002-02-19 00:00:00-05:00 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 4 | 2002-02-20 00:00:00-05:00 | 1.615920 | 1.662210 | 1.603296 | 1.662210 | 6892800 | 0.0 | 0.0 |
In [21]:
url2 = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
html_data_2 = requests.get(url2).text
In [22]:
soup2 = BeautifulSoup(html_data_2, 'html.parser')
In [23]:
table1 = soup2.find_all('tbody')[1]
table1
Out[23]:
<tbody> <tr> <td style="text-align:center">2020-04-30</td> <td style="text-align:center">$1,021</td> </tr> <tr> <td style="text-align:center">2020-01-31</td> <td style="text-align:center">$2,194</td> </tr> <tr> <td style="text-align:center">2019-10-31</td> <td style="text-align:center">$1,439</td> </tr> <tr> <td style="text-align:center">2019-07-31</td> <td style="text-align:center">$1,286</td> </tr> <tr> <td style="text-align:center">2019-04-30</td> <td style="text-align:center">$1,548</td> </tr> <tr> <td style="text-align:center">2019-01-31</td> <td style="text-align:center">$3,063</td> </tr> <tr> <td style="text-align:center">2018-10-31</td> <td style="text-align:center">$1,935</td> </tr> <tr> <td style="text-align:center">2018-07-31</td> <td style="text-align:center">$1,501</td> </tr> <tr> <td style="text-align:center">2018-04-30</td> <td style="text-align:center">$1,786</td> </tr> <tr> <td style="text-align:center">2018-01-31</td> <td style="text-align:center">$2,825</td> </tr> <tr> <td style="text-align:center">2017-10-31</td> <td style="text-align:center">$1,989</td> </tr> <tr> <td style="text-align:center">2017-07-31</td> <td style="text-align:center">$1,688</td> </tr> <tr> <td style="text-align:center">2017-04-30</td> <td style="text-align:center">$2,046</td> </tr> <tr> <td style="text-align:center">2017-01-31</td> <td style="text-align:center">$2,403</td> </tr> <tr> <td style="text-align:center">2016-10-31</td> <td style="text-align:center">$1,959</td> </tr> <tr> <td style="text-align:center">2016-07-31</td> <td style="text-align:center">$1,632</td> </tr> <tr> <td style="text-align:center">2016-04-30</td> <td style="text-align:center">$1,972</td> </tr> <tr> <td style="text-align:center">2016-01-31</td> <td style="text-align:center">$3,525</td> </tr> <tr> <td style="text-align:center">2015-10-31</td> <td style="text-align:center">$2,016</td> </tr> <tr> <td style="text-align:center">2015-07-31</td> <td style="text-align:center">$1,762</td> </tr> <tr> <td style="text-align:center">2015-04-30</td> <td style="text-align:center">$2,061</td> </tr> <tr> <td style="text-align:center">2015-01-31</td> <td style="text-align:center">$3,476</td> </tr> <tr> <td style="text-align:center">2014-10-31</td> <td style="text-align:center">$2,092</td> </tr> <tr> <td style="text-align:center">2014-07-31</td> <td style="text-align:center">$1,731</td> </tr> <tr> <td style="text-align:center">2014-04-30</td> <td style="text-align:center">$1,996</td> </tr> <tr> <td style="text-align:center">2014-01-31</td> <td style="text-align:center">$3,684</td> </tr> <tr> <td style="text-align:center">2013-10-31</td> <td style="text-align:center">$2,107</td> </tr> <tr> <td style="text-align:center">2013-07-31</td> <td style="text-align:center">$1,384</td> </tr> <tr> <td style="text-align:center">2013-04-30</td> <td style="text-align:center">$1,865</td> </tr> <tr> <td style="text-align:center">2013-01-31</td> <td style="text-align:center">$3,562</td> </tr> <tr> <td style="text-align:center">2012-10-31</td> <td style="text-align:center">$1,773</td> </tr> <tr> <td style="text-align:center">2012-07-31</td> <td style="text-align:center">$1,550</td> </tr> <tr> <td style="text-align:center">2012-04-30</td> <td style="text-align:center">$2,002</td> </tr> <tr> <td style="text-align:center">2012-01-31</td> <td style="text-align:center">$3,579</td> </tr> <tr> <td style="text-align:center">2011-10-31</td> <td style="text-align:center">$1,947</td> </tr> <tr> <td style="text-align:center">2011-07-31</td> <td style="text-align:center">$1,744</td> </tr> <tr> <td style="text-align:center">2011-04-30</td> <td style="text-align:center">$2,281</td> </tr> <tr> <td style="text-align:center">2011-01-31</td> <td style="text-align:center">$3,693</td> </tr> <tr> <td style="text-align:center">2010-10-31</td> <td style="text-align:center">$1,899</td> </tr> <tr> <td style="text-align:center">2010-07-31</td> <td style="text-align:center">$1,799</td> </tr> <tr> <td style="text-align:center">2010-04-30</td> <td style="text-align:center">$2,083</td> </tr> <tr> <td style="text-align:center">2010-01-31</td> <td style="text-align:center">$3,524</td> </tr> <tr> <td style="text-align:center">2009-10-31</td> <td style="text-align:center">$1,835</td> </tr> <tr> <td style="text-align:center">2009-07-31</td> <td style="text-align:center">$1,739</td> </tr> <tr> <td style="text-align:center">2009-04-30</td> <td style="text-align:center">$1,981</td> </tr> <tr> <td style="text-align:center">2009-01-31</td> <td style="text-align:center">$3,492</td> </tr> <tr> <td style="text-align:center">2008-10-31</td> <td style="text-align:center">$1,696</td> </tr> <tr> <td style="text-align:center">2008-07-31</td> <td style="text-align:center">$1,804</td> </tr> <tr> <td style="text-align:center">2008-04-30</td> <td style="text-align:center">$1,814</td> </tr> <tr> <td style="text-align:center">2008-01-31</td> <td style="text-align:center">$2,866</td> </tr> <tr> <td style="text-align:center">2007-10-31</td> <td style="text-align:center">$1,611</td> </tr> <tr> <td style="text-align:center">2007-07-31</td> <td style="text-align:center">$1,338</td> </tr> <tr> <td style="text-align:center">2007-04-30</td> <td style="text-align:center">$1,279</td> </tr> <tr> <td style="text-align:center">2007-01-31</td> <td style="text-align:center">$2,304</td> </tr> <tr> <td style="text-align:center">2006-10-31</td> <td style="text-align:center">$1,012</td> </tr> <tr> <td style="text-align:center">2006-07-31</td> <td style="text-align:center">$963</td> </tr> <tr> <td style="text-align:center">2006-04-30</td> <td style="text-align:center">$1,040</td> </tr> <tr> <td style="text-align:center">2006-01-31</td> <td style="text-align:center">$1,667</td> </tr> <tr> <td style="text-align:center">2005-10-31</td> <td style="text-align:center">$534</td> </tr> <tr> <td style="text-align:center">2005-07-31</td> <td style="text-align:center">$416</td> </tr> <tr> <td style="text-align:center">2005-04-30</td> <td style="text-align:center">$475</td> </tr> <tr> <td style="text-align:center">2005-01-31</td> <td style="text-align:center">$709</td> </tr> </tbody>
In [24]:
gme_list = []
for variable in table1.find_all("tr"):
column = variable.find_all("td")
date = column[0].text
revenue = column[1].text
gme_list.append({"Date":date, "Revenue":revenue})
gme_revenue = pd.DataFrame(gme_list)
gme_revenue
Out[24]:
| Date | Revenue | |
|---|---|---|
| 0 | 2020-04-30 | $1,021 |
| 1 | 2020-01-31 | $2,194 |
| 2 | 2019-10-31 | $1,439 |
| 3 | 2019-07-31 | $1,286 |
| 4 | 2019-04-30 | $1,548 |
| ... | ... | ... |
| 57 | 2006-01-31 | $1,667 |
| 58 | 2005-10-31 | $534 |
| 59 | 2005-07-31 | $416 |
| 60 | 2005-04-30 | $475 |
| 61 | 2005-01-31 | $709 |
62 rows × 2 columns
In [25]:
gme_revenue["Revenue"] = gme_revenue["Revenue"].str.replace(",", "").str.replace("$", "")
gme_revenue
Out[25]:
| Date | Revenue | |
|---|---|---|
| 0 | 2020-04-30 | 1021 |
| 1 | 2020-01-31 | 2194 |
| 2 | 2019-10-31 | 1439 |
| 3 | 2019-07-31 | 1286 |
| 4 | 2019-04-30 | 1548 |
| ... | ... | ... |
| 57 | 2006-01-31 | 1667 |
| 58 | 2005-10-31 | 534 |
| 59 | 2005-07-31 | 416 |
| 60 | 2005-04-30 | 475 |
| 61 | 2005-01-31 | 709 |
62 rows × 2 columns
In [26]:
gme_revenue.dropna(inplace=True)
gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]
gme_revenue
Out[26]:
| Date | Revenue | |
|---|---|---|
| 0 | 2020-04-30 | 1021 |
| 1 | 2020-01-31 | 2194 |
| 2 | 2019-10-31 | 1439 |
| 3 | 2019-07-31 | 1286 |
| 4 | 2019-04-30 | 1548 |
| ... | ... | ... |
| 57 | 2006-01-31 | 1667 |
| 58 | 2005-10-31 | 534 |
| 59 | 2005-07-31 | 416 |
| 60 | 2005-04-30 | 475 |
| 61 | 2005-01-31 | 709 |
62 rows × 2 columns
In [27]:
gme_revenue.tail()
Out[27]:
| Date | Revenue | |
|---|---|---|
| 57 | 2006-01-31 | 1667 |
| 58 | 2005-10-31 | 534 |
| 59 | 2005-07-31 | 416 |
| 60 | 2005-04-30 | 475 |
| 61 | 2005-01-31 | 709 |
In [28]:
gme_data
Out[28]:
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2002-02-13 00:00:00-05:00 | 1.620128 | 1.693350 | 1.603296 | 1.691667 | 76216000 | 0.0 | 0.0 |
| 1 | 2002-02-14 00:00:00-05:00 | 1.712707 | 1.716074 | 1.670626 | 1.683251 | 11021600 | 0.0 | 0.0 |
| 2 | 2002-02-15 00:00:00-05:00 | 1.683250 | 1.687458 | 1.658002 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 3 | 2002-02-19 00:00:00-05:00 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 4 | 2002-02-20 00:00:00-05:00 | 1.615920 | 1.662210 | 1.603296 | 1.662210 | 6892800 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5936 | 2025-09-17 00:00:00-04:00 | 26.350000 | 26.469999 | 25.580000 | 26.100000 | 7897100 | 0.0 | 0.0 |
| 5937 | 2025-09-18 00:00:00-04:00 | 26.209999 | 26.420000 | 25.840000 | 25.879999 | 7806200 | 0.0 | 0.0 |
| 5938 | 2025-09-19 00:00:00-04:00 | 25.830000 | 26.389999 | 25.790001 | 26.080000 | 9567600 | 0.0 | 0.0 |
| 5939 | 2025-09-22 00:00:00-04:00 | 26.000000 | 27.340000 | 25.969999 | 26.780001 | 11263600 | 0.0 | 0.0 |
| 5940 | 2025-09-23 00:00:00-04:00 | 26.905001 | 27.240000 | 26.590000 | 26.799999 | 6638965 | 0.0 | 0.0 |
5941 rows × 8 columns
In [29]:
gme_revenue
Out[29]:
| Date | Revenue | |
|---|---|---|
| 0 | 2020-04-30 | 1021 |
| 1 | 2020-01-31 | 2194 |
| 2 | 2019-10-31 | 1439 |
| 3 | 2019-07-31 | 1286 |
| 4 | 2019-04-30 | 1548 |
| ... | ... | ... |
| 57 | 2006-01-31 | 1667 |
| 58 | 2005-10-31 | 534 |
| 59 | 2005-07-31 | 416 |
| 60 | 2005-04-30 | 475 |
| 61 | 2005-01-31 | 709 |
62 rows × 2 columns
In [30]:
make_graph(tesla_data, tesla_revenue, "Tesla")
/tmp/ipython-input-109047474.py:5: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. /tmp/ipython-input-109047474.py:6: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
In [31]:
make_graph(gme_data, gme_revenue, "GameStop")
/tmp/ipython-input-109047474.py:5: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. /tmp/ipython-input-109047474.py:6: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.